AIMC Topic: Azo Compounds
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Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD.
Annals of diagnostic pathology
Apr 12, 2020
Accurate detection and quantification of hepatic fibrosis remain essential for assessing the severity of non-alcoholic fatty liver disease (NAFLD) and its response to therapy in clinical practice and research studies. Our aim was to develop an integr...
Fibrosis
Azo Compounds
Collagen
Pathologists
Supervised Machine Learning
Non-alcoholic Fatty Liver Disease
Clinical Trials as Topic
Organ Dysfunction Scores
Eosine Yellowish-(YS)
Methyl Green
Automation
Portal Vein
Practice Patterns, Physicians'
Humans
Artificial Intelligence
Severity of Illness Index
Liver
Biopsy
Image Processing, Computer-Assisted
Liver Cirrhosis
High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.
Arthritis research & therapy
Mar 14, 2020
BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRS...
Microbial Decolorization of Triazo Dye, Direct Blue 71: An Optimization Approach Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN).
BioMed research international
Feb 18, 2020
The release of wastewater from textile dyeing industrial sectors is a huge concern with regard to pollution as the treatment of these waters is truly a challenging process. Hence, this study investigates the triazo bond Direct Blue 71 (DB71) dye deco...
Single and competitive dye adsorption onto chitosan-based hybrid hydrogels using artificial neural network modeling.
Journal of colloid and interface science
Oct 29, 2019
Chitosan-based hybrid hydrogels such as chitosan hydrogel (CH), chitosan hydrogel with activated carbon (CH-AC), scaffold-chitosan hydrogel (SCH), scaffold-chitosan hydrogel with activated carbon (SCH-AC) and scaffold-chitosan hydrogel with carbon na...
Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches.
Journal of environmental management
Jul 24, 2019
Textile industry wastewaters, which cause serious problems in the environment and human health, include synthetic dyes, complex organic pollutants, surfactants, and other toxic chemicals and therefore must be removed by advanced treatment methods. De...
Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network.
Journal of environmental management
Jun 27, 2018
Presence of pigments and dyes in water bodies are growing tremendously and pose as toxic materials and have severe health effects on human and aquatic creatures. Treatments methods for removal of these toxic dyes along with other pollutants are growi...
A micro-plate colorimetric assay for rapid determination of trace zinc in animal feed, pet food and drinking water by ion masking and statistical partitioning correction.
Food chemistry
Oct 14, 2017
A new micro-plate colorimetric assay was developed for rapid determination of zinc in animal feed, pet food and drinking water. Zinc ion was extracted from sample by trichloroacetic acid and then reacted with 2-(5-Bromo-2-pyridylazo)-5-[N-propyl-N-(3...
Spectrophotometric determination of synthetic colorants using PSO-GA-ANN.
Food chemistry
Oct 4, 2016
Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeli...
A knowledge-based expert rule system for predicting mutagenicity (Ames test) of aromatic amines and azo compounds.
Toxicology
Sep 16, 2016
Cancer is one of the main causes of death in Western countries, and a major issue for human health. Prolonged exposure to a number of chemicals was observed to be one of the primary causes of cancer in occupationally exposed persons. Thus, the develo...
Classifying Force Spectroscopy of DNA Pulling Measurements Using Supervised and Unsupervised Machine Learning Methods.
Journal of chemical information and modeling
Apr 4, 2016
Dynamic force spectroscopy (DFS) measurements on biomolecules typically require classifying thousands of repeated force spectra prior to data analysis. Here, we study classification of atomic force microscope-based DFS measurements using machine-lear...
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